Drought- and heat-induced mortality of conifer trees is explained by leaf and growth legacies

An increased frequency and severity of droughts and heat waves have resulted in increased tree mortality and forest dieback across the world, but underlying mechanisms are poorly understood. We used a common garden experiment with 20 conifer tree species to quantify mortality after three consecutive hot, dry summers and tested whether mortality could be explained by putative underlying mechanisms, such as stem hydraulics and legacies affected by leaf life span and stem growth responses to previous droughts. Mortality varied from 0 to 79% across species and was not affected by hydraulic traits. Mortality increased with species’ leaf life span probably because leaf damage caused crown dieback and contributed to carbon depletion and bark beetle damage. Mortality also increased with lower growth resilience, which may exacerbate the contribution of carbon depletion and bark beetle sensitivity to tree mortality. Our study highlights how ecological legacies at different time scales can explain tree mortality in response to hot, dry periods and climate change.

Fig. S6.The minimum water potential during the growing season as measured for shoots (Pmin) versus the embolism resistance (P50).The hydraulic safety margin (HSM) is the vertical distance between the points and the 1:1 dashed, diagonal, line.For species abbreviations, see Table S1.Errors bars represent 95% confidence intervals of the mean.2c).This longer time series is equal to the one used in Song et al. 2022 (26) for the same set of species, but the design was not fully balanced since these two early droughts were not included for Pinus armandii, and the 1975-1976 drought not for Abies veichii (Table S1).The results were nevertheless similar, showing a significant decrease in mortality with increased growth resilience to droughts preceding 2018.

Proportional mortality
Fig. S9.Relationships between the presence of bark beetle exit holes on tree stems (on x-axis: no, yes) for different species (see Table S1), an important pest for many conifers, and the investigated three factors   S1.
Table S1.Overview of 20 conifer species, their species name, abbreviation code (as used in some Figures), original regional distribution, the period over which tree rings were analyzed, and the average stem diameter at breast height (DBH) of trees sampled for stem ring and traits measurements (N=10), and its standard deviation (in parentheses).
Distribution areas are obtained from Farjon and Filer (68).Beetle presence was checked based on the presence of exit holes in trees in 2021.Further, we show species specific scores of tree mortality (MR) and of a number of tree rings statistics (see for details on growth resilience methods and results on these species Song et   ), LDMC = leaf dry matter content (in g g -1 ) and SLA = specific leaf area (in cm 2 g -1 ).Table S3.Test statistics (regression coefficients and probability values) of a multiple logistic regression model predicting the mortality risks from the hydraulic safety margin (HSM) , leaf lifespan and growth resilience (Resilience) across 20 conifer tree species.To account for possible collinearity between the predictors leaf life span and resilience, we replaced growth resilience with the residuals of growth resilience against leaf life span using a linear regression model: the results were very similar to the results presented in Table 2, and confirm that leaf life span and growth resilience have at least partially additive effects in explaining tree mortality.

Fig. S4 .
Fig. S4.Patterns of mortality rates in relation to various leaf traits across 20 conifer species.Predictions from logistic models are shown by solid lines if significant (P≤0.05), or dashed lines if not (P>0.05).

Fig. S5 .
Fig. S5.Patterns of mortality rates versus two resilience components across 20 conifer species.Predictions from logistic models are shown by solid lines if significant (P≤0.05), or dashed lines if not (P>0.05).

Fig. S7 .
Fig. S7.Comparison of critical niche borders for high temperature and low rainfall across the 20 conifer tree species studied.For species codes, see Table S1.MAT and Tmax stand for the mean annual temperature and the maximum monthly temperature within the geographic distribution range of each species respectively, and MAP and Pmin for mean annual precipitation and minimum monthly precipitation respectively.The symbols indicate the 50% quantile values and the error bars the range from 10% to 90% quantiles.The gridlines indicate the conditions for the common garden study site at the Schovenhorst Estate forest, the Netherlands.The climate distribution ranges of species were obtained from the Global Biodiversity Information Facility GBIF: https://gbif.org/(for details, Song et al. 2022 (28)).

Fig. S8 .
Fig. S8.Conifer species comparisons for patterns of mortality in response to the 2018 drought year with growth resilience to 8 dry periods preceding the 2018 drought, thus adding the 1975-1976 and 1982-1983 droughts to the series from 1986 (see Figure 2c).This longer time series is equal to the one used in Song et al. 2022 (26) for the same : a) leaf life span (Student T-Test T=-5.66,P<0.01), b) Resilience (Student T-Test T=-2.89,P=0.01), and c) hydraulic safety margin (Student T-Test T=-0.37,P=0.72) for the 20 tree species (N=20).Bars represent species averages and standard errors are added.

Fig. S10 .
Fig. S10.Patterns of mortality rates versus tree size or age across 20 conifer species.The predictions from logistic models were non-significant and are therefore shown as dashed lines (P≤0.05),Species abbreviations are explained in TableS1.

Table S2 .
al. 2022 (26): average tree ring width (TRW), Rbar which indicates inter tree ring series autocorrelation, EPS which indicates expressed population signals and MS which indicates sensitivity.Correlation coefficients for relationships between mortality, growth resilience and functional plant traits The bold and underlined correlations indicate P<0.01 and bold correlations indicate 0.01≤P<0.05.